WO2002037234A2

WO2002037234A2 - System and method for collaborative order fulfillment

Info

Publication number: WO2002037234A2
Application number: PCT/US2001/050706
Authority: WO
Inventors: Kenneth B. Brown; Gerald S. Russell
Original assignee: Brown Kenneth B
Priority date: 2000-10-20
Filing date: 2001-10-19
Publication date: 2002-05-10
Also published as: WO2002037234A3; AU2002234141A1

Abstract

A system and method are disclosed for retailers, wholesalers, and manufacturers of goods to anonymously collaborate in buying and selling order fulfillment contracts and options on order fulfillment contracts, for the purchase and delivery of inventory items. The system and method permit businesses to incur lower order fulfillment logistics costs while maintaining or improving customer service levels by using a dealer trading module (36) and system trading module (42) to sell order fulfillment contracts and options thereon. A dealer logistics planning module (32) uses information from dealer inventory (16), dealer forecasts (30), and system quotes (38) databases to develop a logistics action plan (34) before trading order fulfillment contracts or options thereon. Entities may buy and sell options for future deliveries of inventory. Use of the present invention may reduce inventory-carrying costs and order fulfillment transportation costs resulting in improved efficiency and profitability, while maintaining or improving customer service levels.

Description

SYSTEM AND METHOD FOR COLLABORATIVE ORDER FULFILLMENT

Field of Invention

The present invention relates to a trading system and an inventory management system to work in conjunction with a trading system for order fulfillment contracts. More particularly, the present invention relates to trading systems for collaborative order fulfillment of contracts for the purchase and delivery of inventory items. Background-Description of Prior Art In many retail stores, inventory management is still handled using simple, time-honored methods, which includes basic "arithmetic," i.e., adding and subtracting. Sale volumes for each item may be measured on a daily, weekly, or monthly basis. With knowledge of these sales volumes, regular orders for inventory may be placed in accordance with regular demand. When supplies run low for a particular item, that item may be replaced on a special-order basis. Alternatively, such an item may be ordered in a standard quantity whenever its inventory level falls to a pre-specified level. The standard quantity for inventory replenishment is chosen on the assumption that it will last for an approximate, predetermined length of time. The order quantity, and the desired inventory level, may be adjusted occasionally, in accordance with predicted future demand. The methods that have been described suffer from significant inefficiencies. For example, at times, when a new shipment has just arrived, the inventory level may be too high which causes unnecessarily high working capital investments in inventory, interest expense, storage space, insurance, and taxes. Furthermore, there will be unnecessarily high risk that the items will become obsolete, unfashionable, or stale before they can be sold. There is a different effect later in the inventory cycle when the stocks are low. At this time, there is a great likelihood that a sudden random increase in short-term demand will cause an out- of-stock condition. This may result in lost sales and/or customer dissatisfaction, and/or loss of reputation as a "good" supplier.

It is well known that both manufacturers and wholesalers hold inventories, and face many of the same basic issues that concern retail businesses. These manufacturers and wholesalers also are concerned with high capital and storage costs, and risks of inventory obsolescence. An additional concern is that there may be a shortage of parts, which will result in expensive plant shutdowns; therefore, it is critical to maintain at least minimum inventory levels. In order to control inventory and storage costs, "Just-In-Time" (JIT) inventory systems have been developed. Typically, these systems are based on a "probability distribution function" (PDF) model of the variability of the rate of demand. Given the PDF, a minimal level of inventory may be chosen to guarantee any predetermined level of probability that the item will be in stock. Inventory items are shipped to the selling entity at the mean sales rate, on a regular and frequent schedule. The shipping schedule may be adjusted, as frequently as necessary in accordance with demand, to maintain the minimum inventory level. In addition, a continuous feedback system may be used to update the contents of each regular shipment. JIT inventory systems, however, have proved inefficient in the sense that they require multiple shipments of inventory items at regular or irregular intervals. Therefore, it may be difficult to achieve economies of scale in shipping costs. Businesses must consider the trade-offs (and benefits) of lower inventory cost versus greater shipping costs. Because of these tradeoffs, JIT inventory systems usually only create a competitive advantage for very large retail entities that have certain amounts of leverage with their suppliers.

Recent innovations in inventory management have been related to the use of distributed computer systems to process the necessary information. For example, U.S. Patent No. 5,404,291 to Gordon S. Kerr et al. and U.S. Patent No. 5,581,461 to Denise Coll et al. describe data handling networks for managing the hotel room inventory across nationwide hotel chains, supporting information exchange between distributed remote locations and a central hub. Also U.S. Patent No. 5,596,493 to Kaoru Tone et al. Describes a system that uses "point-of-sale" ("POS") terminals to gather sales volume data, as well as the types of demand estimation procedures, to generate orders for replenishing inventory. U.S. Patent No. 5,712,989 to James M. Johnson et al., on the other hand, describes a system that generates purchase orders in accordance with JIT guidelines using a host computer, and one or more local computers operated by Customer Service Representatives. U.S. Patent No. 5,940,807 to Daniel S. Purcell describes an inventory information exchange system. This system allows multiple sellers to provide information about their available inventory, and, further, allows potential buyers to review pertinent information and locate the products in the volumes required. Among other things, the systems that have just been described do not create a secure and private method for buyers and sellers to coordinate their inventory holdings and to cooperate to minimize inventory expenses while maximizing customer satisfaction.

The Internet has revolutionized the way business is transacted. The Internet makes it possible for even small retailers, with or without an actual physical storefront, to sell products to customers all around the world. With regard to inventory control, Internet merchants may maintain inventory levels using traditional or JIT methods. This will be done in the same manner as a locally- based merchant. When a product is sold by an Internet merchant, it is typically shipped from the Internet merchant's warehouse directly to the customer, who may be many miles away. Alternatively, the Internet merchant may prefer to have the product "drop-shipped" from a wholesaler or manufacturer.

There does not appear to be practical commercial enterprises to efficiently and automatically work cooperatively with remotely located sources of inventory for fulfilling orders to customers. Logistics, specifically transportation and inventory management, amount to huge sums annually for a company. In 1996, according to "CLI's 8th Annual State of Logistics Report" (1997), freight costs for all sectors in the United States amounted to approximately $451 billion, and inventory carrying costs for all sectors was approximately $311 billion. Just these two costs are over 10% of the Gross Domestic Product (GDP) for 1996.

The very high costs just discussed are a result of competitive shipping and inventory management decisions made by many thousands of merchants, whose fundamental competitive interest is in individually controlling access to customers, rather than cooperation to maximize product distribution efficiency. As such, there is an opportunity to increase inventory efficiency by providing a method for commercial enterprises to work cooperatively in meeting inventory requirements.

In order to facilitate this cooperation among merchants, which may lead to tremendous new efficiencies in logistics management, consideration must be given to the development of trading systems for order fulfillment contracts and options on order fulfillment contracts. In its most basic form, futures trading involves both producers and users of a commodity product, and the schedule and flow of deliveries at a predetermined price levels. However, commodities futures contracts are, and have been, highly inflexible in terms of delivery arrangements, which have typically defined in terms of pickup and delivery at a single warehouse. Thus, most commercial users of future contracts typically close them before the fulfillment date, and then arrange to actually complete a similar transaction with a local trading partner on the spot market. Futures systems, even with the options, would be very inconvenient for controlling of inventory.

Participants in the futures markets are usually experienced in financial risk management and speculative participation. The understanding of these subjects is important in evaluating whether to permit commercial interests to off-load risk of market price changes. Options contracts have been developed as a more pure method of risk management and transfer of risk among parties.

Prior art methods relating to futures and options trading has been directed to computerization and automation of such trading. For example, U.S. Patent No. 4,903,201 to Susan Wagner is directed to a system having multiple remote terminals connected to a central computer. According to this patent, each remote terminal may be used to enter orders to buy or sell futures contracts. U.S. Patent No. 5,557,517 to Vergil L. Daughterty IE is directed to the problem of estimating the correct bid and asks prices for options on a variety of securities. U.S. Patent No. 5,950,176 granted to Timothy M. Keiser and Michael R. Burns provides a method for automating the function of a specialist market-maker for a securities exchange, by matching buy and sell orders, and responding to imbalances. U.S. Patent No. 6,016,483 to John T. Rickard and William A. Lupien is similar to the '176 patent, but specifically addresses imbalances that occur at the opening of trading on an options exchange.

The prior art references that have been described above, do not provide a system or method to minimize the cost of order fulfillment logistics through instituting a marketplace for order fulfillment contracts, and options on order fulfillment contracts, in which commercial enterprises transparently cooperate to match existing and planned inventory levels with future inventory requirements.

Nor do such systems provide an inventory management system to help commercial enterprises to make optimal business decisions while transacting in such a marketplace.

Summary of the Invention

The present invention is a system and method for merchants (retailers), wholesalers, and manufacturers of goods to anonymously collaborate in buying and selling order fulfillment contracts and options on order fulfillment contracts, for the purchase and delivery of inventory items. The system and method of the present invention permits businesses to incur lower order fulfillment logistics costs while maintaining or improving customer service levels. More specifically, entities may buy and sell options for future deliveries of products for inventory that may be used with on-hand physical inventory to form the total inventory for that entity. Commercial enterprises, which includes retailers, wholesale distributors, and manufacturers may use the present invention to reduce inventory-carrying costs and order fulfillment transportation costs resulting in improved efficiency and profitability, while maintaining or improving customer service levels.

The system and method of the present invention are "anonymous" from the standpoint that merchants are not required to directly interact to facilitate the efficiency improving transactions. Instead, certain transactions are arranged, managed, and cleared by the system and method, for example, through a clearinghouse to maintain the confidentiality between the actual buyer and seller that have the existing relationship. In addition, the system of the present invention includes a novel method for protecting sensitive customer data.

The present invention reduces the amount of inventory required to maintain a specified service level. To illustrate, if a merchant sells a product at an average rate of mi units per week, and that the actual sales rate fluctuates weekly about this average, the assumption made is that these sales fluctuations are normally distributed, then the weekly product sales is given by Expression (1):

X, =«, +6 (1) where, nn = The predicated future sales per week for retailer "i". ξ. = The unanticipated demand for the week for retailer "f. This variable is based on N (0, σ_t ) such that the variable is a normally distributed random variable with a mean of 0 and a standard deviation of σ, . In order to assure that the product will be in stock throughout an entire order cycle with a probability SL , referred to as "service level," the retailer must maintain safety stock ssi in addition to the predicted future sales m,-. The safety stock will be at a level that will insure that the retailer will maintain a predetermined service level. This will mean that the total inventory at the beginning of each order cycle will be mi + s_t. If for a given week X,- < * + ss i.e., the actual sales rate is lower than the beginning inventory, then the retailer will not run out of stock. However, if X,- > * + ssi, i.e., the actual sales is greater than the beginning inventory, then the retailer will run out of stock, thereby causing delayed or lost orders. If this occurs, the retailer will run out of stock, thereby causing delayed or lost orders.

To determine the appropriate service level, Expression (2) is used:

where, m; = The predicted demand per week for retailer "f. σf = The variance in demand for the week for retailer "i".

Xi = The observed demand per week for retailer "f ' . ssi = The safety stock for the week for retailer "?^'".

In the above example, a safety stock of ssi = 2σ_i will result in a 97.72% service level.

The present invention, unlike the prior example, provides n retailers with the ability to pool their inventories to obtain a reduction in physical inventory among the retailers, increased efficiency in product delivery, and a reduction in shopping costs. These advantages will be described in detail subsequently. Weekly sales fluctuations experienced by each retailer is not correlated with the sales fluctuations experienced by the other retailers, the aggregate product sales will be given by Expression (3): n

X_τ = (m_i +ξ_i) = m_τ +ξ_τ (3) ι=l where, m_t = The predicted future sales per week for retailer "f . ξ, = The unanticipated demand for the week for retailer "z". niτ = The total predicted future sales per week in terms of "n" number of retailers. ξ_τ = The total unanticipated demand for the week in terms of "n" number of retailers. n = The number of retailers. ξ_τ , which is a normally distributed random variable with a mean of "0" and a standard deviation of σ_τ , may be written according to Expression (4) for cases of non-correlated sales fluctuations:

where, σf = The variance in demand for the week for retailer "ϊ". σ_τ = The standard deviation of demand for the week of the aggregate sales. n = The number of retailers. For simplicity, if the assumption is that the n retailers have the same mean sales rates and sales rate fluctuations, i.e., m; = m and σ_t = σ for all n retailers, then it follows that Expression (4) may be written as shown in Expression (5):

To determine the appropriate service level in the aggregate case, the following Expression is used:

where,

_<7_y = The variance in demand for the week of the "n" number of retailers. X_T = The total observed aggregate demand for the week in terms of "n" number of retailers. iτ = The total predicted aggregate for the week in terms of "n" number of retailers. s γ = The aggregate safety stock for the week for "«" number of retailers.

To assure a 97.72% service level, the safety stock required is according to Expression (7): ss_τ = 2σ_τ = 2σ_i n ⁽J) where, ST = The aggregate safety stock for the week for "n" number of retailers. σ_τ = The standard deviation of demand for the week of the aggregate sales. σ, = The standard deviation in demand for the week for retailer

"f.

This safety stock burden can be shared equally among the n retailers. Under this method, each retailer would carry safety stock in an amount according to Expression (8):

where, ss_t = The safety stock for the week for retailer "f '. σ, = The standard deviation of demand for the week for retailer

Expression (8) provides for a reduction in safety stock by a factor of

^■ jn

This is an example of a reduction that may be realized by the present invention over the original safety stock of ssi = 2σ. indicated above that is needed to achieve a 97.72% service level. This reduction is significant, especially if a large number of retailers participate in inventory pooling. As an example of the magnitude of the significance, if n, the number of retailer equals 100, the safety stock amount for each retailer is reduced by a factor of 10.

According to the present invention, the collaboration necessary to realize this safety stock reduction may be accomplished through an on-line or a subscription trading system. Taking the last example with a reduction by a factor of 10 as the operating base, each merchant may purchase real inventory in an amount according to Expression (9): m, + ^ units (9)

where, mi = The predicted demand for the week for retailer "/". ss_t = The safety stock for the week for retailer "t". n = The number of retailers.

The retailer "z" would then purchase call options on order fulfillment contracts from other traders in order to raise the total effective inventory (real inventory + virtual inventory) to m, + SS_J. Therefore, each retailer has access to the resources of the entire group of system subscribers to meet product demand without having the need to carry all of the inventory on-hand.

The present invention also provides an opportunity for sellers to collaborate to reduce shipping costs via shipping arbitrage, especially in cases where shipping costs are significant compared to total product cost. As an example, if two on-line retailers sell the same item and an on-line shopper purchases an item from Retailer 1, but that shopper lives much closer to Retailer 2, the cost of shipping the item would be changed from Retailer 1 to Retailer 2. That is, it costs an amount C_γ to ship the item from Retailer 1 to the shopper, whereas it costs C_% to ship the item from Retailer 2 to that same shopper, and Ci > C2- To save on shipping costs, Retailer 1 can perform shipping arbitrage by purchasing an order fulfillment contract from Retailer 2, who subsequently fulfills the ordered item to the on-line shopper from his local inventory. This arrangement generates net savings of Cj - C₂ in shipping costs, which could be shared between the two retailers in some predetermined manner. One additional point to note is that after the transaction, Retailer 1 will have one extra inventory item in stock and Retailer 2 will be short one inventory item. This situation does not pose a problem because, at a later time, Retailer 1 can engage in another arbitrage transaction in which he or she acts as the fulfiller to unload the extra inventory. Moreover, Retailer 2 will have an opportunity to act as the arbitrageur, using another retailer's inventory to cover his temporary product shortage. This arbitrage activity between Retailers 1 and 2 is done through a clearinghouse to maintain anonymity.

By monitoring the spot price of fulfillment contracts, retailers can detect and anticipate developing system-wide shortages of a product (which is indicated by rising prices), and take proper actions, such as (i) increasing the retail price of the product, (ii) substituting other products, or (iii) temporarily removing the product from a catalog. By utilizing the trading system of the present invention, retailers can reduce the rate of stock-outs, which results in customer dissatisfaction. The present invention has other novel features with regard to inventory, control. Some dealers with high-cost fulfillment may decide to become what is referred to as "Pure Play" retailers. Retailers of this type rely entirely on the order fulfillment network to fulfill all of their orders and do not carry any physical inventory of their own. Therefore, the other dealers who actually hold physical inventory will become larger and more efficient. Because of their size, these large- inventory holding entities may find that they can schedule more frequent shipments of products from the manufacturer, while still achieving economies of scale. These more frequent and larger shipments will again reduce the average length of time that a product spends in the warehouse, thus reducing the inventory-carrying cost per unit.

In order to gain economies of scale in manufacturing and shipping, commercial enterprises often place orders for products in large quantities at regular time intervals or according to a cyclic pattern. Immediately after the arrival of a product shipment, the quantity of product in stock far exceeds the quantity of safety stock required on an instantaneous basis to prevent stock-outs. As a consequence, the instantaneous rate of inventory carrying cost is much higher than desired early in the cycle. Later, as the time of the next shipment draws closer, the amount of capital tied up in inventory is much lower, but there is a dramatically increased risk of stock-outs.

Dealers can use the present invention to mitigate some of the most costly effects of the replenishment cycle. For example, early in the cycle, dealers having excess inventory on hand may choose to sell fulfillment contracts and options on the trading market, in order to realize some income from their excess inventory. This trading policy will result in an accelerated delivery schedule from inventory while stocks are large. Later in the cycle, as the amount of physical inventory stock decreases, the remaining inventory will be used only to service the most profitable direct orders.

Virtual inventory contracts may be purchased as necessary to fill orders at the end of the replenishment cycle, preventing the catastrophic effects of stock- outs. The overall effect of this policy will be to reduce the average length of time that each item remains in the inventory, thus cutting average inventory levels and costs for dealers, while drastically reducing or eliminating the risk of stock-out. An object of the present invention is to provide a "spot market" for trading of fulfillment contracts for immediate delivery of inventory items. These inventory items are not limited to consumer good, or manufactured or commodity item of standard specification. Another object of the present invention is to provide a system and method that permits merchants with an immediate need to have a product delivered to a customer, to anonymously contract with a fulfillment entity or other inventory owner who promptly ships the product to the customer. The use of an independent fulfiller located close to the customer will permit the merchant to save on shipping costs. And, any merchant with an immediate need to sell inventory directly from stock can do so promptly and securely.

A further object of the present invention is to provide a system and method that permit merchants anticipating a possible need for inventory, to purchase options for inventory items ("virtual inventory") to be delivered only if the anticipated requirement actually materializes. Such an inventory will be significantly less expensive than real inventory until the option is exercised, while being just as effective for meeting the specified customer service level. Upon exercise, an option contract on inventory converts into an order fulfillment contract for immediate delivery of inventory. A yet further object of the present invention is to provide a system and method that permits merchants, wholesalers, or manufacturers possessing inventory items, to realize additional revenue against that inventory by selling "virtual inventory" options to other merchants, thus reducing the effective costs of inventory. Another object of the present invention is to provide a system and method to reduce the overall size of the pool of inventory needed to meet customer satisfaction requirements across the set of all "virtual inventory" providers and users. This will reduce inventory carrying costs, including capital costs, storage costs, risk of obsolescence, and shrinkage. A still further object of the present invention is to provide a system and method that permits access to geographically distributed, national and global, inventory available for delivery; which is especially useful for merchants whose customers are highly geographically distributed so that rather than stocking inventory at a few centralized locations, these merchants will be able to save tremendous shipping costs by purchasing "virtual inventory" that is geographically close to each customer.

Another object of the present invention is to provide a system and method that will maintain a database of all products offered for sale, with full descriptions, images, and attributes.

A still further object of the present invention is to provide a system and method that will track for a retailer all sales of each item and predict future sales rates of each item.

A yet further object of the present invention is to provide a system and method that will maintain for a retailer a record of all special marketing promotions related to each product, either actual or planned, and to track and predict the effects of these special promotions on sales and profitability.

A still yet further object of the present invention is to provide a system and method that will maintain for a retailer a database of existing and planned inventory, to forecast future inventory requirements.

Another object of the present invention is to provide a system and method that will make logistics strategy recommendations for ordering real inventory, for purchasing order fulfillment contracts, for purchasing options on order fulfillment contracts, and for writing and selling options on order fulfillment contracts. A further object of the present invention is to provide a system and method that will provide for a dealer a convenient mechanism to verify and exercise options on order fulfillment contracts; that is, to send the proper instructions and schedule shipment of ordered items when options have been exercised.

A still further object of the present invention is to provide a system and method that will identify for a dealer opportunities for profitable transactions on the "spot market" for order fulfillment contracts.

A yet further object of the present invention is to provide a system and method that will maintain a database of all products offered for sale, with full descriptions, images and attributes, across all manufacturers whose products are traded through the system.

A further object of the present invention is to provide a system and method that will maintain a database of all past and predicted future product sales across all system subscribers. This data may be used to enhance the quality of forecasting for individual dealers, and it may also be used to generate activity reports for dealers.

Another object of the present invention is to provide a system and method that will maintain a record of past and planned marketing promotions for each product across system subscribers and to track and predict the effects of these marketing promotions on sales and profitability, including the effects of one merchant's promotional efforts on the sales of other merchants.

A still further object of the present invention is to provide a system and method that will maintain a database of the existing and planned inventory across all system subscribers, to forecast future inventory requirements.

A yet further object of the present invention is to provide a system and method to facilitate modeling of strategies for logistics management, including real and virtual inventory.

A further object of the present invention is to provide a system and method that will provide estimates of the appropriate cost levels for inventory option contracts.

A yet further object of the present invention is to provide a system and method that will maintain a database of all open, exercised, and expired options on order fulfillment contracts. A still further object of the present invention is to provide a system and method that will evaluate the performance of individual merchants, wholesalers, and manufacturers in satisfying the terms of the contracts.

Another object of the present invention is to provide a system and method that will provide a mechanism to handle payments to and from system subscribers for transactions on the system.

These and other objects will be set forth in detail in the remainder of the specification referring to the drawings. Brief Description of the Drawings

Figure 1 is a diagram of an embodiment of the global optimization system of the present invention.

Figure 2 is a representative system user interference and menu tree for use with the present invention. Figure 3 is a representative flow diagram for a retailer purchasing an order fulfillment contract or exercising a call option on an order fulfillment contracts according to an embodiment of the present invention.

Figure 4 is a representative flow diagram for a fulfiller selling a fulfillment contract according to an embodiment of the present invention.

Figure 5 is a representative flow diagram for fulfiller selling call options for order fulfillment contracts according to an embodiment of the present invention. Detailed Description of the Invention

The present invention is system and method for trading of fulfillment contracts and options on fulfillment contracts to provide "virtual inventory" for businesses to efficiently handle and control inventory along with physical inventory. According to this system and method, merchants (retailers), wholesales, and manufacturers of products may anonymously collaborate in buying and selling order fulfillment contracts and options on order fulfillment contracts for the purchase of inventory items. The present system and method result in reduced inventory-carrying costs and order fulfillment costs. This improves efficiency and profitability, while maintaining or improving customer service levels. Moreover, the method and system of the present invention for anonymously conducting trading operations may be used in other than trading relating to order fulfillment contracts or options for order fulfillment contracts.

In the preferred embodiment of the present invention, the system users of the present invention, which may be merchants (retailers), wholesalers, manufacturers, distributors or fulfillment houses (collectively referred to as "dealers"), use computer terminals to enter relevant information about their businesses. This information may include but not be limited to offered products, product sales, and inventory levels. The system utilizes this information to help the dealers to evaluate the status of their businesses, and to develop strategic plans for logistics management and inventory control. The system also enables each system user to consummate anonymous trades with other system users for order fulfillment contracts and options on order fulfillment contracts. Preferably, all information may be transmitted over a global communications network, e.g., the Internet, to a centralized computer network, which performs the necessary system operations, maintains the centralized databases, and facilitates trading. The present invention provides each system user with a unique and isolated environment, having carefully managed communications with common areas of the system. By using a global communications network, such as the Internet, the only software required at the user's computer terminal will be a standard browser, such as Netscape Navigator® or Microsoft Internet Explorer®. Netscape Navigator is a registered trademark of Netscape Communication Corporation and Internet Explorer is a registered trademark of Microsoft, Inc.

The preferred embodiment of the invention may use machine-executable instructions, which may be executed on general-purpose or special-purpose processors. However, the functions of the system user's computer terminal, as well as the functions of the central computer, also may be performed by specific hardware components that contain hard- wired logic for performing the steps, or by any combination of programmed computer components and hardware components. Similarly, the communications between system user terminals and the central computer may be performed by any conventional communication method that may be used between digital-based systems.

The present invention may be provided as a computer-based product which may include a machine-readable medium having stored thereon instructions which may be used to program a computer (or other electronic devices) to perform a method of the present invention. The machine-readable medium may include, but is not limited to, floppy diskettes, optical disks, CD-ROMs, and magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, magnet or optical cards, or other type of media/machine-readable medium suitable for storing electronic instructions. Moreover, the present invention may also be downloaded, as a computer program product. As such, the program may be transferred from a remote computer, e.g., a server, to a requesting computer, e.g., a client, by way of data signals embodied in a carrier wave or other propagation medium via a communication link, e.g., a modem or network connection.

The system of the present invention, as stated, may be computer-based, and as such, its structure is closely related to its operation. The operation of the system according to the method of the invention is set forth in the "Operation" section found subsequently in the specification. Client-Server Environment

The preferred client-server environment for the system and method of the present invention is the World Wide Web (the "Web"). Although, it is preferred, it is only an exemplary client-server environment in which on-line system operation is accomplished according to the system and method of the present invention. The Web, which may be characterized generally as the "Internet," is conventional when used use in the context of the system and method of the present invention is new and novel. It is to be understood that other client-server systems besides the Internet may be used and still be within the scope of the invention. The use of the terms "client" and "server" in the context of the present invention is to refer to a computer's general role as a requester of data (the client) or provider of data (the server). Preferably, Web servers are coupled to a global communications network and respond to document requests and/or other queries from Web clients. The server delivers the requested document, typically in the form of a text document coded in a standard markup language, such as hypertext Markup Language (HTML).

Exemplary Computer System

A computer system that may embody client-server environment described above may include a bus or other communication system for communicating information, and a processing system, such as microprocessor or other type of processor, coupled with the bus for processing information. The computer system also includes a random access memory (RAM) or other dynamic storage device, which may be characterized as the main memory. This main memory may be coupled to the bus, for storing information and instructions to be executed by the processor. The main memory also may be used for storing temporary variables or other intermediate information during execution of instructions by processor. The computer system may have a read only memory (ROM) and/or other static storage device coupled to the bus for storing static information and instructions for the processor. A data storage device, such as a magnetic disk or, optical disc and its corresponding drive for storing information and instructions may be connected to the processor.

The computer system will also include a display device for displaying information for the system user. Moreover, the computer system will include input device for inputting information and/or command selections to the microprocessor. Another type of system user input device would provide cursor control. This device includes but is not limited to a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to the microprocessor. The computer system may have a communication device for accessing remote servers via the global communication network. The communication device includes, but is not limited to, a modem, a network interface card, or other commercially available network interface devices, such as those used for coupling to an Ethernet, token ring, or other type of network. The communications device permits the computer system of the present invention to be connected to a number of clients and/or other servers via a conventional network infrastructure, such as a company's Intranet a LAN, MAN, WAN, or Internet. Basic System Overview The system of the present invention shown generally at 9 to Figure 1. The preferred embodiment includes algorithms and data structures, which are implemented as computer instructions running on a server computer. A system user may be a product retailer, wholesaler or manufacturer (referred to collectively as a "dealer"). A dealer who is a system user will provide data to, and receive information from, the server computer through a user interface module implemented on a client computer. This client computer is connected to the server computer the global communications network, which may be an Intranet, a LAN, a MAN, a WAN, or the Internet. The system user interface module will be described in more detail with respect to Figure 2.

Although, there are a number of system users, the identity of each user is confidential with regard to the other system users for the purpose of carrying out transactions according to the present invention. More specifically, this confidentiality and anonymity includes the system data that each system user provides and extends to order fulfillment contracts and options for order fulfillment contracts. Referring to Figure 1, each of the modules that must be replicated for each dealer, include the prefix "Dealer" in the module name. On the other hand, the modules that are implemented only once for the entire system of the present invention, and shared by all of dealers include the prefix "System" in the module name. The system of the present invention includes a number of databases. Preferably, each of these databases may be implemented as SQL (Structured Query Language) databases, or as a multidimensional database (for performing OLAP (On-line Analytical Processin ) processes) such as those provided by Oracle, Inc. The implementation and use of SQL databases is conventional.

Referring to Figure 1, data relating a particular dealer's product sales is maintained in Dealer Sales History Database 20. This information may include the item sold, time and date of the sale, customer (if known), and price. Data relating to the dealer's marketing campaigns associated with particular products is maintained in Dealer Marketing History Database 18.

Information in the two databases just described is input by the system user, or alternatively, gathered by a point-of-sale (POS) terminal in the case of sales data and then input to Dealer Modeling Module 22. Dealer Modeling Module 22 uses information from Dealer Sales History Database 20 and Dealer Marketing History Database 18 to generate a predictive model of future sales of a product as a function, for example, of recent sales information, long-term sales information, and marketing campaign information. The generated model also takes into account seasonal variations in sales rates, as well as effects related to aging and obsolescence of product designs. Further, the model also describes the geographical distribution of product sales.

Methods for implementing the Dealer Modeling Module 22 include, but are not limited to, regression analysis, exponential smoothing, neural network modeling, genetic algorithms, and other predictive techniques. The parameters of the predictive model generated for each product is stored in the Dealer Models Database 24.

The parameters for the predictive model that are stored in Dealer Models Database 24 for each product are input to Dealer Forecasting Module 28. The second input to Dealer Forecasting Module 28 is the dealer's planned future marketing promotions information for the product at issue from Dealer Planned Marketing Database 26. Dealer Forecasting Module 28 uses the parameters from Dealer Models Database 24 and planned future marketing promotions stored in Dealer Planned Marketing Database 26 to estimate future demand and future demand variability for each product. The resulting information that is stored in Dealer Forecast Database 30 is the predicted future demand levels for each product.

Information a] out each product is maintained in System Product Information Database 10. Further, information about each dealer is maintained in System Dealer Information Database 12. Information from these two databases is input to Dealer Dynamic Product Costing Module 14, which calculates the dealer's inventory costs. This module is described in more detail below. The data from Dealer Dynamic Product Costing Module 14 is stored in Dealer Inventory Database 16. The Information output from Dealer Dynamic Product Costing Module 14 that is stored in Dealer Inventory Database 16 includes current inventory levels and estimated costs, including capital costs, storage costs, obsolescence costs and shrinkage. Alternatively, the inventory availability information may have been manually entered by the system user, or may have been generated by point-of-sale (POS) terminals using automated data receiving equipment, such as bar code readers.

The inventory cost estimates that are stored in Dealer Inventory Database 16 are used by the dealer to calculate a reasonable asking price for order fulfillment contracts that will be placed for sale on the spot market. A reasonable asking price is assumed to be an asking price that gives the dealer a contribution margin large enough to generate the required return on investment over long term product sales and cost projections.

Information relating to the availability and pricing of spot market inventory and options on spot market inventory is stored in the System Quotes Database 38. In a system with a "market-maker," firm market price quotes will be available. In a system with no "market-maker," quotes .will consist of unmatched buy and sell offers at various prices, and quotes from historical transactions. When a "market- maker" is referred to it is intended to mean an entity who will provide liquidity to the system by meeting trading requirements from his her own inventory, if necessary. The information for System Quotes Database 38 is the first input to

Dealer Logistics.

As stated, data on current inventory levels and inventory carrying costs is stored in Dealer Inventory Database 16. This information is the first input to

Dealer Logistics Planning Module 32. The second input to Dealer Logistics Planning Module 32 is the data on forecasts of future sales and estimated variability of sales stored in Dealer Forecasts Database 30. The third input to Dealer Logistics Planning Module 32 is the data on pricing and availability of spot market inventory and options on spot market inventory stored in System Quotes Database 38.

Dealer Logistics Planning Module 32 processes the input information to generate a logistics action plan. The logistics action plan will include the optimal inventory strategies, given a particular dealer's constraints and rules. These constraints and rules advise how the dealer should buy, sell, and utilize real inventory, spot market inventory, and options on spot market inventory throughout the planning horizon or cycle. The Dealer Logistics Planning Module 32 will be described in more detail subsequently.

Each of the dealer logistics action plans that is generated is stored in Dealer Logistics Action Plan Database 34. When required, specific Dealer Logistics Action Planning as transmitted from Dealer Logistics Action Plan Database 34 to Dealer Trading Module 36. This takes place when a dealer is seeking to perform transactions for buying and selling order fulfillment contracts and options for order fulfillment contracts using the system and method of the present invention, as will be described in detail subsequently. This action also takes place when the dealer is seeking to replenish on-hand inventory.

As described, System Quotes Database 38 provides the third input to Dealer Logistics Planning Module 32. Now, the information that is stored in this database will now be described.

System Pricing Module 40 estimates the costs of option contracts based on spot market pricing data. Preferably, this module uses a Black-Scholes model or binomial model, as discussed in further detail below.

The estimates generated by System Pricing Module 40 are the first input to System Quotes Database 38. The second input to System Quotes Database 38 is the output of System Pricing Module 40, which is in a feedback loop. The output of System Trading Module 42, which applies across all system users, matches bids for order fulfillment contracts and options on order fulfillment contracts with offers as they are generated by the Dealer Trading Modules 36. The quotes output from

System Quotes Database 38 are system wide quotes. It is to be noted that the output of System Quotes Database 38 is fed to System Pricing Module 40 for the purpose of performing the Black-Scholes calculation by providing theoretical prices to this module.

The output of Dealer Trading Module 36 is input to System Trading Module 42, which as described above, matches bids for inventory option with offers for such inventory options. There is a Dealer Trading Module 36 for each dealer. The second input to System Trading Module 42 is the system wide quotes for Systems Quotes Database 38.

System Trading Module 42 optionally may include a "market-maker" function that accepts a reservoir of order fulfillment contracts and options on order fulfillment contracts, and delivers them to buyers as required. In another embodiment, bids and offers that cannot be immediately filled are simply left in a waiting buffer until they can be completed. In either case, matched bids and offers for inventory options result in completed contracts, which are stored in the System Contracts Database 44. The System Contracts Database 44 may be considered the final result of the preferred embodiment. Additional facilities required for contract fulfillment, such as option exercising, mailing label generation for shipment, and label scanning for receiving inventory items delivered under this program handled in a conventional manner. Dealer Dynamic Product Costing Module (14)

Referring to Figure 1, Dealer Dynamic Product Costing Module 14 is used to dynamically calculate current product costs and predict future product costs. These calculations preferably are based on the following underlying cost factors: 1. The total cost of the product includes producing, transporting, and handling, and it is represented by p_pwduct = P acquisition + Ptmnspon + Phandimg- These categories, in detail, are the following:

(a) P_acqui_sιti_on(q,t) - Product acquisition costs. For manufacturers, this quantity will be the product's standard or actual direct manufacturing cost including relevant setup costs. Alternatively, this quantity may be the manufacturer, manually input costs. For wholesalers and retailers, this quantity will be the unit purchasing price from the manufacturer including quantity discounts and ordering costs. This quantity is represented as P_acquismon{q,t), which indicates its dependence on order quantity (or production batch size) and time. Once inventory has been acquired, its acquisition cost is recorded in the dealer's inventory database.

(b) P_tmnsp_ort(q,t) - Product transport costs. The costs associated with moving the acquired product to the final storage location. The economies of scale in transportation reduce the unit transportation cost as the total shipment size increases. Therefore, it is often more cost effective for dealers to take delivery of a few large shipments of products as opposed than many small shipments. This quantity is represented as P_tmmport{q,t), which indicates its dependence on quantity and times.

(c) Piιandiing(q,t) - Product handling cost. - The handling cost per unit is estimated as the total labor cost of operating the warehouse or storage facility, divided by the number of items that have been moved into and out of storage. This quantity is represented as p_handUng {q,t) , which also indicates its dependence on quantity and time.

2. Product carrying costs - The cost associated with the level of on- hand inventory. These costs are divided into four categories that can be represented by r_carry = r capital + Service + f i k, which are discount factors:

(a) ^_capi_tal (^f ) " The opportunity cost of capital. The dealer determines this either by reviewing its corporate financial statements, by doing comparable analysis, or by using the capital asset pricing model. This quantity is represented by r _iua it) , which indicates its dependence on time. ) S_ervi_ce ( " The service costs, such as ad valorem taxes, fire insurance, and theft insurance, that are usually dependent upon the average amount of inventory in stock. This quantity is represented by r_seιvice(t), which also indicates its dependence on time.

(^c) ^r _storaSe ( " The storage cost related to level of inventory. The storage cost per unit volume may be calculated from the total cost of owning the warehouse or storage facility for a defined period of time

(including fixed maintenance costs) divided by the volume of items that may be stored in it. From this, the unit storage cost for each product line may be calculated. This quantity is represented by r (t) , which indicates its dependence on time.

(d) ^r _ri _k (t) ~ The inventory risk costs, such as risk of obsolescence, shrinkage, and damage. These costs are often related to inventory level. Obsolescence costs are costs incurred at the end of a product's life cycle, when the products remaining in stock are either sold to customers at reduced prices, or disposed of through other means. Shrinkage costs are the costs of stolen or lost goods. These costs may vary as a function of product and time. This quantity is represented by r_risk (t) , which indicates its dependence on time.

The risk of loss due to obsolescence may vary during the course of a product's lifetime. Specifically, it may be very low immediately after introduction, and grow gradually with time. The length of a product's lifetime, and the rate of increase of obsolescence risk, may be estimated based on historical experience with products in the same category. Nalues for r_risk (t) might be assigned by product or by product category.

Given the foregoing parameters, the unit carry costs is determined by Expression (10):

C ⁼ (r carry) ( P product) . ^■ (10) As shown above, r_carry and p_product have a number of elements and to fully report the carry costs, the expanded Expression (10) is shown in Expression (11):

C

+ P transport + P handling) (*■ *■)

When the desire is to determine the value of inventory for calculating carrying costs, the process is to multiply the units of each product in inventory by carry cost per unit (c).

When there is an evaluation for a particular dealer, generally, the cost functions will decrease as the order quantity and the total quantity of inventory processed increases. Due to factors, such as the availability of warehouse space and labor for handling, the costs of inventory may start to increase with increases in quantity at some point, or there may be a physical limit on the maximum amount of inventory that may be stored at a location. These factors and constraints may be coupled with the additional terms, which must be considered in inventory determinations. System Pricing Module (40)

System Pricing Module 40 is used for estimating options prices, based on quotes available for current spot market prices from System Quotes Database 38. Considering the input just described, option pricing may be calculated using Black- Scholes equation that has been to account, modified for the carrying cost and the right to exercise the option at anytime before it expires. This is represented by Expression (12):

lt^{~ +} 2 dS^{T +} ^^{interest + r y} ' 3S ^{~ rinterest ( }} where, V = The change of the option that is a function of S, t, σ , E, T,

Y Interest, Tcariy- dv_

The change of the option price (value) with respect to time. dt t = Time, σ = The volatility of the underlying asset ( σ as it is used here is not the same as σ defined previously).

E = The exercise price of the option.

T = The expiration time.

S = The spot market price of the underlying asset. f i_nterest = The risk free interest rate in the currency (money) of the underlying asset. r_carιy = The carrying cost.

The parameter V may have different values given the time that options are exercised. When there is an early exercise of the options, the payoff is P(S,t). This will mean that the constraint Vwill be according to Expression (13): V{S,ή ≥ P{S,t) (13) where,

S = The spot market price of the underlying asset. t = Time.

In other words, the value of an option at a given time must always be greater than or equal to the payoff upon exercise of the option. The constraint posed by Expression (13) applies until the option expires or is exercised. However, at expiration of the option period, the final condition is represented by the Expression

(14): V(S,t) = P(S,t) (14) where,

S = The spot market price of the underlying asset. t = Time. There are conventional methods to determine Expressions (13) and (14) under the early exercise condition and final condition. In addition, there are equivalent conventional techniques, such as the binomial model, that may be used to derive and solve options pricing issues that lead the same result as the Black- Scholes model. All of these techniques yield satisfactory results, as long as reasonably accurate values are used for the model parameters.

Appropriate Nalues for Model Parameters

The variables that have been described for Expression (13) will now be explained in greater detail. The spot market price, S , is dictated by the balance of supply and demand, and may be influenced by the existance of market-marker(s). The volatility as set forth in the section titled "System Pricing Module (40) is reported as σ . This is a measure of spot market price fluctuations and is estimated according to Expression (15):

where S(t₍. ) = The spot market closing price on day t_t .

M = The number of measures. t = Time.

The risk free interest rate, r_interest, is the yield of the U.S. long bonds at a particular point in time or time period. The carrying cost, r_carry, is calculated by performing a weighted average of the carrying costs for all dealers in the system. The value of the remaining parameters, E and T , are specified by the trading exchange which performs the trading transactions for the system and method of the present invention in order to assure demand for the options.

Dealer Logistics Planning Module (32) Dealer Logistics Planning Module 32 informs a system user of optimal inventory and trading policies on a predetermined basis. This could be on daily, weekly, monthly, or user defined basis. The information that is provided is in the context of a business' capabilities and resources profile (business constraints), and user defined goals.

Typically, dealers prefer steady growth. This is based on the trade-off between potential profitability and potential risk. The logistic planning system enables the system user to utilize the business' constraints and goals, and in particular, sales, to optimize profits while at the same time minimizing risk. In addition, the system does not preclude skillful system users from taking advantage of industry knowledge and market intuition in order to optimize their actual returns. The projections and recommendations made by the logistics planning system are advisory and do not obligate the dealer to take any particular course of action.

The trading system of the system and method of the present invention accumulates a large amount of varied historical data, including product sales history, warehouse facilities, fulfillment capabilities, carrying costs, economical order quantities, manufacturer prices, typical retail prices, and profit margins.

Each dealer may have a different cost basis for physical inventory. Even in light of this, the system predicts product sales as a function of past sales, product pricing, geography, and marketing based on the accumulated historical data. In particular, geography plays an important role in determining the optimal physical inventory source for actual delivery from networked virtual inventory sources. The system provides this information so that Dealer Logistics Planning Module 32 can guide the system user to trade successfully with other dealers in order to achieve improved profitability.

The Logistics Action Plan and Decision Parameters Given the sales projections and business constraints, the goal of logistics planning is to provide in a cost-effective manner the resources necessary to meet the expected demands as they materialize. Specifically, the logistics action plan will be based on a schedule of purchases for physical inventory. The planning of these purchases (which tend to be bulk purchases in order to achieve economies of scale in manufacturing and shipping) establishes a cyclic reality that must be attributed to any planning process. That is, at times when purchases are scheduled, the system will anticipate the mix of real inventory, virtual inventory, and spot market purchases, which will be used to meet predicted customer demand, while concurrently optimizing profits and minimizing as much risk as possible. The trading system of the present invention gives the dealer the flexibility to purchase physical inventory much less than to much greater than the expected level of sales to customers. The difference between physical inventory and actual demand is accommodated by purchases or sales using the trading network. Inventory is available for purchase on the trading network in two forms: order fulfillment contracts, and options on order fulfillment contracts (for minimizing risk and for speculation). Quotes for both forms of inventory are available on the system. If the dealer purchases greater inventory than needed, then dealer may make additional profits by selling the excess on the trading system. If the dealer purchases less inventory than needed, he can use the trading system to obtain additional inventory to meet any shortfall.

Inventory also may be sold or optioned onto the market at any time. Options written and sold on order fulfillment contracts may or may not be exercised, depending on factors, such as customer demand behavior and spot market price fluctuations. Upon exercise, options contracts will effectively convert to order fulfillment contracts for immediate product delivery. As an example, information that is received by Dealer Trading Module 36 may include a complete logistics action plan, which is re-assessed regularly. This plan may consist of expected daily product sales directly to customers, expected product sales onto the spot market, expected outstanding option contracts, expected number of spot market contracts purchased from the spot market, and expected inventory on hand. However, it is understood that more or less information may be received as part of the logistics action plan and still be within the scope of the present invention. Each of these variables discussed is a function of time throughout the replenishment cycle (the time between real inventory replenishment). The logistics action plan also may include an estimate of the economical order quantity, planned date of the next order of physical inventory, and planned date of arrival of next physical inventory order. The merchant's expected profit is a function of these variables, as well as, the projected spot market prices, options market prices, and actual retail sale prices.

Throughout the replenishment cycle, the logistics action plan may be re- evaluated based on new information or events. An example of an event that may cause a re-evaluation of the plan is a consistent and/or significant deviation of actual sales from forecasted sales. Such a deviation may require remodeling to generate new forecasts and a re-evaluation of the logistics action plan to determine the optimal policy under these new conditions. Revisions to the plan may also be triggered by large changes in the price of inventory or the price of inventory options in the trading market. The logistics planning system does not affect the rate of product sales to customers. However, the system and method of the present invention provides immediate and essentially continuous control over the number of virtual inventory options contracts that are outstanding, the quantity or rate of product sales into the order fulfillment spot market, and the date and quantity of the next order to replenish physical inventory. The initial order quantity also may be determined by Dealer Logistics Planning Module 32 at the beginning of each replenishment cycle. The quantity of physical inventory between orders may be regulated by the purchase of spot market order fulfillment contracts whenever the physical inventory level drops below the target level. Further, it is possible to view total sales as consisting of product sales to customers, product sales resulting from conversions of inventory options written, and product sales through the spot market. Then, the logistics system may use spot market sales (or purchases) to regulate total sales within predetermined bounds.

Noting the foregoing, the parameters to be determined by the logistics optimization system are:

(1) The initial quantity of physical inventory to be purchased.

(2) The quantity of real and virtual inventory to be maintained throughout the replenishment cycle.

(3) The total rate of product sales as a function of time, summed over retail customers, as well as, sales into the trading market.

(4) The timing of the next order for bulk physical inventory replenishment.

These parameters, defined throughout the replenishment cycle, are the "decision parameters" which will be discussed in detail subsequently. Inventory System Structure

In order to determine an optimal set of "decision parameters," which will define an effective logistic action plan, Dealer Logistics Planning Module 32 must build a complete model of the factors that determine the profitability of the dealer in the marketplace. This model includes: (i) the structure that describes the relationships between the parameters and variables of the model; (ii) the decision parameters, which determine the recommended trading strategy; (iii) the system parameters, which are observable measurements and projections of external inputs to the system; (iv) the random variables, describing the actual events such as price changes and trades which cannot be precisely predicted in advance; (v) the constraints, which set limits on the values of the parameters and variables; and (vi) an objective function (in this case, the dealer's profitability) which yields the quantity to be optimized. This structure will now be discussed in greater detail.

For a given model and a particular set of decision parameters, the expected profit and variance may be estimated through Monte Carlo simulation. The decision parameters that optimize the expected value of the objective function can be found using conventional optimization methods, such as linear programming, simulated annealing, genetic algorithms, or conjugate gradient descent.

In general, the optimal logistics action plan will depend on the specifics of the dealer's situation. However, certain common circumstances may be anticipated. In general, dealers with a low cost structure for inventory storage will tend to order much more inventory than they expect to sell to their own customers. When a shipment of physical inventory arrives, these dealers will seek to maximize revenues from that shipment by selling inventory options in order to realize income from their inventory investment. They may also sell inventory directly into the spot market, since their accumulated inventory carrying costs at that point are very low and they will find that they can profitably sell into that market. Later in the replenishment cycle when their cumulative inventory carrying costs are higher, these dealers will tend to hold inventory only for delivery based on options contracts or for their own customers.

Dealers with high inventory carrying costs may decide not to carry any inventory at all. These dealers may either purchase virtual inventory contracts to get guaranteed cost levels, or else may rely entirely on the "spot" market for their inventory needs. The dealers' levels of risk tolerance, and their profitability goals, will be an important factor in determining optimal strategies. Dealers with higher risk aversion will sell fewer options contracts earlier in the cycle, and will buy options contracts to guarantee their costs late in the replenishment cycle. Dealers aiming for the highest possible expected profit, regardless of risk, will sell more options contracts and will rely more heavily on the spot market for inventory purchases.

Dealer insights into future price trends may be used to help determine optimal inventory and trading strategies. More specifically, dealers expecting price declines will sell options and purchase spot market inventory, while dealers expecting price increases will purchase options and physical inventory, and sell inventory into the spot market as prices rise.

Decision Parameters

The optimal strategy for the logistics action plans are represented in terms of decision parameters, which define the appropriate actions in response to various conditions at all phases of the replenishment cycle.

For a single product, the decision parameters for a replenishment cycle are: - ^_ordq (t'S'I) ~ The recommended order quantity for real inventory at time t. 2. SALES_Vi_rtuai(t,S,I) - The recommended number of virtual inventory

(call options on order fulfillment contracts) to sell at time t.

3. SALES _spot(t, S, I) - The recommended number of order fulfillment contracts to sell on the spot market at time t.

4. PURCHASE_Vi _aι(t,S,I) - The recommended number of virtual inventory (call options on order fulfillment contracts) to buy at time t.

5. PURCHASE_spot(t,S,I) - The recommended number of order fulfillment contracts to buy on the spot market at time t.

The decision parameters above are expressed as a function of time, t, spot market sales price, S, and inventory level, I. Preferably, these are the most significant factors that may effect decisions on a day-to-day basis and cannot be precisely forecasted in advance. However, the decision parameters also may be functions of other variables as well, or constants with respect to any of these variables. As such, the decision parameters may be extended to include additional parameters. Alternatively, any of the decision parameters defined above may be set to a fixed value, or a function of one of the other decision parameters. Any of these variants on the definition of the decision parameters would be understood to be within the scope of the present invention. Since some optimization procedures require an enumerated set of variables as parameters, rather than functions, the decision parameters set forth above may be represented by a specific set of parameters using a number of conventional methods. For example, time t, spot price S, and inventory level / may be expressed in terms of discrete intervals, for example, t=l to T. If this is the case, the decision parameters would be defined at each of these discrete intervals. At high levels of resolution for time, spot market sales price, and/or inventory level, representation of the decision parameters could result in a very large set of decision parameters. If a smaller set of parameters is desired for purposes of defining the optimization, the decision parameters may be expressed as generic polynomial or spline functions of a few parameters. For purposes of description the splines referred to have smooth curves, which connect between specified points on a continuous function.

System Parameters The present invention has system parameters that are used in the optimization process of the present invention. The following are system parameters that are considered. These normally are fixed (not variable) with respect to a particular optimization of the decision parameters. However, these parameters can change value from one optimization run to another. 1. SL: A customer service level specified by a retailer based on the overall marketing and customer satisfaction strategy.

2. ri_ntere_sf- The interest rate specified by the dealer for use over the forecast horizon for discounting future cash flows.

3. r_cany: The carrying cost for each unit of real inventory for each time increment calculated by Dealer Dynamic Product Costing

Model 14.

4. O: The direct and indirect costs for ordering real inventory from Dealer Dynamic Product Costing Model 14.

5. LT: The typical length of time for inventory (lead time) to arrive once an order is placed.

6. σ_LT : The standard deviation of the lead time. Objective Function

Typically, an objective function calculates a quantity to be optimized by the choice of decision parameters. In the case of the present invention, the objective function is used to calculate profit. Accordingly, the daily profit PROFIT(t) is calculated based on the sum of several profit source terms. As such, profit may be represented by Expression (16):

PROFrr(t) = PR Fπ_realcust + PROFΠ ,_ΨO, + (16)

PROFIT _{spo ιCUSt} + PROFTT_opti_ons where, PROFrr_reα/,_{CM =} The profit from sales to customers using real inventory for fulfillment.

PROFIT reai pot = The profit from selling order fulfillment contracts on the spot market.

PROFrT._sp0f;C„_ιsf = The profit from selling to customers using spot market inventory for fulfillment.

PROFTT_op_tion = The profit from sales and purchase of virtual inventory.

Each of the profit terms may be calculated from the sales price and the underlying asset cost. The average daily profit over the replenishment cycle is the objective function. Although maximization of profits is described here, it is understood that the maximization of other parameters, such as "Return On Assets" is within the scope of the present invention.

Constraints

In accordance with the present invention, there are certain constraints that are limiting factors with respect to the decision parameters and system variables. These constraints prevent the decision parameters and system variables from taking on values outside of specified ranges.

Since there is some level of randomness in product pricing in a trading market, the profits associated with a particular logistics action plan cannot be predicted precisely. Higher-risk strategies (more random in outcome) may be associated with higher expected profitability. However, in many cases, a dealer may be risk averse, and therefore wishes to limit risk exposure. This constraint is represented by 7_prgfit , which is the maximum amount of risk that the dealer is willing to bear. Other constraints that preferably are considered may include the availability of capital for purchase of inventory, the availability of warehouse space for storing inventory, and the availability of labor for handling inventory.

Projecting Future Demand and Prices There is the use of an appropriate forecasting technique, such as exponential smoothing, neural networks, or ARIMA, to generate predicted product sales over the decision horizon. The present invention uses a preferred process for projecting future demand and future prices. According to the present invention, there is first an estimate of the future spot market price for order fulfillment contracts over the decision horizon. Then, using the Black-Scholes options pricing equation, there is the calculation of the estimates for the future options prices over the decision horizon. The factors that are considered with regard to projecting future demand and future prices are the following:

1. Demand - Given product sales history, planned marketing programs, and industry projections, the Dealer Forecasting Module 28 provides a forecast of future product sales rate over the specified forecasting horizon. The forecast is accompanied by an estimate of its variance. d{t),σ_d ²{t) where, d(t) = Future product sales rate. σ if) = Estimate of its variance.

2. Estimates of the Future Spot Market Price for Order Fulfillment Contract - Typically, the spot market price for an order fulfillment contract will reflect a small value-added percentage over the manufacturer's sale price, to reflect expenses of shipping, handling, carrying cost, and addition to a profit for the fulfiller. The spot market price typically will be lower than the retail price. This will allow some profit for the retailer. Occasionally, unexpected variations may occur in the spot price due to unanticipated changes in supply and demand. The spot market price is represented by S(t . The price for a product may be modeled using a stochastic differential equation defining a random walk with a systematic drift trend. This is represented by Expression (17). dS = σ_sSdX +μ(t)Sdt (17) where,

S = The spot market price. μ(t) = The drift function (this may be used to model systematic price changes due for example, to obsolescence or seasonality) or other. σ_s = The volatility in the spot price of the order fulfillment contract. dX = A normally distributed random variable with a mean of "0" and a standard deviation of dt . dt = The Change in time.

Typical sequences of market prices may be generated by the repeated application of the model set forth in Expression (18) using different sequences of random variation. 3. Estimates of the Future Option Price on an Order Fulfillment

Contract - A corresponding sequence of option prices may be generated using the Black-Scholes options pricing technique defined in the section titled "System Pricing Module 40" above. This is represented by V(t). Optimization Now that the system parameters, constraints, and projections have been described, the next step in the process is to determine the values for the decision parameters to optimize the profit function. The decision parameters to be determined are Ψ_ordq (t,S,I), PURCHASE_virtual(t,S,I), PURCHASE_spot(t,S,I),

SALES_Vi_rtuai(t,S,I), SALES_spot(t,S,I). Standard linear and/or nonlinear optimization techniques, such as linear programming, quadratic programming, conjugate gradients, simulated annealing, neural networks or genetic algorithms may be used.

In order to evaluate the profit and variance of profit for any given parameterization, multiple pricing realization sequences may be generated using a "Monte Carlo" approach to determine the mean and variance. The profit, however, must be calculated daily, and summed over the replenishment cycle. For each pricing realization sequence, a random sequence of customer sales, fulfillment orders and options conversions also may be generated in conformance with the rate parameters, and then market actions carried out in conformance with the decision parameters. Alternatively, techniques in stochastic calculus may be used to solve directly for the profit and variance of profit as a function of the parameters. Any conventional optimization method and evaluation method may be used and still be within the scope of the present invention. The model may be simplified in various ways, including not using the random aspects of the pricing sequence, or substituting deterministic pricing models. If the model can be expressed in purely linear sequential terms, then the Simplex methods for solving constrained linear systems may be used.

An Example of A User Interface Configuration and Menu Tree Referring to Figure 2, the user interface configuration and menu tree module is shown generally at 50 and is preferably implemented in a client/server architecture. In the preferred embodiment, this module may be implemented in a server computer. A client computer, connected to the server computer by means of an information network which include the internet, uses the interface configuration and menu tree to effect receiving and interpreting inputs from the user, transmitting information data and instructions to the server computer, receiving data and instructions from the server computer, and displaying data, reports, recommendations, and contents. There may be other methods for implementing the user interface other than what is shown in Figure 2. Such methods include, but are not limited to, methods that are not client/server Web-based architectures. According to the preferred embodiment, the client-server architecture will permit the system user to enter via the keyboard of his client system, a URL (Universal Resource Locator). The URL will refer tot he address of the server computer, which provide the inventory options trading service. The URL request is relayed to the Internet by the browser software running on the client machine, where it is routed to the options trading server. This server computer then generates a sequence of HTML (Hypertext Markup Language) commands which, when interpreted by the client computer's Internet browser software, cause an image of a "home" page to be generated on the monitor of the client computer. The design of such a "home" page may include menu selection items, attractive image graphics, and text providing basic information about the options trading service.

The menu selections may include the items Search Box 60, About Us 62, Policies

& Procedures 64, Products & Services 66, Alerts 68, Information Services 70,

Strategic Planning 72, Demo 74, Open New Account 76, Account Log-on 78, and

Customer Service 80. Each of these selections may provide selectable menu items as shown in Figure 2. It is understood that additional menu items or alternative variants on this menu structure may be provided and still be within the scope of the present invention.

Search Box 60, when activated permits the user to specify an English- language or Non-English-language query and to find related text anywhere in the site structure. Search Box 60 may be implemented using an inverted index or other conventional method.

About Us menu 62, when actuated, generates the representative menu item list as shown. The About Us menu permits the system user to access textual information describing the service.

Policies and Procedures menu 64 when activated generates the representative menu item list that is shown. Policies and Procedures menu 64 permits access to more textual information covering quotes, order handling, trades execution and checks and balances. Products & Services menu 66, when activated generates the representative menu item list that is shown. Products & Services menu 66 provides information about accounts, information services, trading, cash management, and insurance and inventory management services provided by the system.

Alerts menu 68, when activated generates the representative menu item list shown. Alerts menu 68 is used to post recent news about the system. Information Services menu 70, when activated, generates the representative menu item list shown. Information Services menu 70 provides utility functions for system users to obtain financial information.

Strategic Planning menu 72, when activated, generates the representative menu item list that is shown. Strategic Planning menu 72 permits the system user to interact with Dealer Logistics Planning Module 32 and Dealer Trading Module 36, which may also run on a central server at an ASP (application service provider), or in-house.

In the preferred embodiment, the server computer issues HTML instructions that causes text entry boxes to be drawn on the user computer screen. The user then inputs numerical entries via the keyboard in order to communicate requested information.

Demo menu 74, when activated, provides a series of static and/or dynamic screens that describes the capabilities of the system. New Account menu 76, when activated permits a user to create a new account. Account Login menu 78, when activated, permits a user to log-in to the system, and provide identification and authentication information. Customer Service menu 80, when activated, permits the user to send messages to a company representative. Operation

Referring to Figures 3, 4, and 5, Operation of the system of the present invention will be described. The examples that are being used to describe the system and method of the present invention are representative and not meant to limit the present invention. The examples are only meant to illustrate the trading system's ability to meet inventory requirements with improved efficiency compared to the prior art.

Example 1: Order Fulfillment Outsourcing

This describes the system with respect to a Web-based retail merchant. However, the example also may apply to a wholesaler who desires to. deliver a product to a "brick-and-mortar" retailer who is implementing a Just-In-Time ("JIT") inventory system.

Referring to Figure 3, the system is initiated at start 106. After the system is initiated, the retailer configures the system by inputting the information to Dealer Logistics Planning Module 32. The information that is input about past sales, estimated product costs, and planned marketing promotions for their products. Dealer Logistics Planning Module 32 is also supplied with information about the retailer's internal inventory levels of the product, and carrying costs. With the assistance of the Dealer Logistics Planning Module 32, the retailer generates forecasts for product sales at 108. When the forecasts are generated, the retailer then schedules the purchase of the optimal mix of real inventory, spot market inventory, and options on spot market inventory as shown at 110. During the operation of the system of the present invention, Dealer Logistics Planning Module 32 recovers system quotes from System Quote Database 38 and the availability of inventory throughout the trading network. After this, as shown at 112, the retailer is prepared to receive an order for a product to be delivered to a customer. Based on the information input to Dealer Logistics Planning Module 32, it makes recommendations regarding the optimal source of inventory for each order that must be fulfilled, when, as shown at 112, an order is received.

Possible inventory sources include real inventory, spot market inventory, and options on spot market inventory. The system is configured so that the retailer may choose to take or not take the recommendation of the Dealer Logistics Planning Module. As such, it is a business decision for the retailer.

The decision-making process is shown as steps 114, 116 and 120. Once an order is received at 112, the retailer at 114 identifies the optimal fulfillment source. If the source is the options market as shown at 116, then the retailer will exercise his/her virtual inventory call option to obtain the goods at 118, and execute a fulfillment contract at 124 for the goods. The parties who transact business with regard to fulfillment contracts are not necessarily known to each other.

If it is not an options market source, then it must be determined if it is a spot market source as shown at 120. If it is, the retailer will purchase a fulfillment contract on the spot market at 122, and the fulfillment contract is formed by the parties at 124 under the conditions described above.

If it is not a spot market source, the order will be filled from the retailer's on-hand inventory at 126. Once this is done, the Global Logistics Optimization Web ("GLOW") system updates the system database at 132 to reflect the change in the retailer's physical inventory. Often the system transmits the actions from this order at 132.

If the order was filled from either virtual inventory or from the spot market, the actual fulfiller fulfills the order at 128. There is dealer anonymity to protect customer data when the fulfiller fulfills the fulfillment contract. This is accomplished by the system using a clearinghouse for fulfilling orders. As such, in reality, at 129, which is within 128, the fulfiller receives instructions from the clearinghouse. These instructions include the product sold, quantity, and the encrypted customer information and address. The fulfiller selects, packs, and labels the ordered products according to the instructions. The label as configured will contain the encrypted customer information and address. The encrypted information may be in the form of a barcode, smartcard, optical transmitter, radio transmitter, wireless transmitter, or other feasible means for encrypting information and address to the products being sold. Once it is packaged in this way, the products are ready for processing by the carrier.

The products that have been packed are then picked-up by the carrier at 131. The carrier has the appropriate equipment to decode the encrypted information. For example, the carrier may have a special label reader that decodes the encrypted customer information and address.

This special label reader may be either a "smart" or "dumb" device. If it is a "smart" device, it will have special processing capabilities to enable it to decode the encrypted information and address without any outside input. However, if it is a "dumb" device without this capability, it may use a service provided by the clearinghouse to decode the information. A representative service may include reading the label optically or otherwise, transmitting the information to the clearinghouse for decoding, receiving the decoded information from the clearinghouse, and the carrier displaying the decoded information on a display. Once the decoded information is in the hands of the carrier, the carrier may print a new label and place it on the package, if desired, or if necessary. Next, the carrier will deliver the package and report back to the clearinghouse and/or the fulfiller that it has been delivered. In any reporting to the fulfiller, the carrier does not include the customer to whom the package was delivered to maintain anonymity of the customer, and the seller. Next, at 130, the retailer receives a confirmation that the order was fulfilled. Following, this, the GLOW system updates the system databases at 132 with the change in inventory and contracts. Now, at 134, the system terminates the transaction with regard to this order. Further, with regard to the operation just described, if the retailer (or wholesaler) decides that the order is to be fulfilled from the spot market at 120, the retailer at 122 will purchase a fulfillment contract on the spot market. When this happens, the order is entered into the trading module, which places it either directly with a fulfillment house or other holder of inventory or indirectly through a market-maker (if present). Funds to pay for the order are deposited with a clearinghouse at this point. The fulfillment house (or other inventory holder) then delivers the product to the customer of the retailer in step 128, by the process described presently which includes the encryption of information 129 and carrier based activities at 131. By using this method described above, shipping costs are reduced if the fulfiller is geographically closer to the customer than the retailers. Furthermore, the retailer has reduced his need to hold inventory, by effectively outsourcing order fulfillment responsibility to a fulfiller on the trading network. Example 2: Providing Order Fulfillment Outsourcing

Referring to Figure 4, this example describes the situation when a fulfillment house wishes to sell some of its inventory promptly. This example may also apply to a retailer, wholesaler, or manufacturer with fulfillment capabilities.

The system is initiated at 150. At this point, the fulfillment house will have provided Dealer Logistics Planning Module 32 with information about past sales, estimated product costs, and planned marketing promotions for products. Dealer Logistics Planning Module 32 subsequently provides the fulfiller with a logistics action plan that helps the dealer make optimal inventory management and trading decisions. System Trading Module 42 provides the fulfiller with information about inventory levels across the trading network, and costs and availability at various locations. Based on this information, Dealer Logistics Planning Module 32 makes recommendations regarding to quantity and pricing of spot market inventory and options on spot market inventory to be placed on the trading network by the fulfiller. As stated with regard to the first example, the fulfiller does not have to take these recommendations. The fulfillment contract specifications, and the desired price and quantity, are entered into the system at 152 by the fulfiller.

If the fulfiller decides to place a fulfillment contract onto the spot market, then the placement is entered into system trading module 42. System Trading

Module 42 posts this information on the trading network as shown at 154. System Trading Module 42 then awaits the arrival of a matching order from a retailer on the network who requires an inventory fulfillment contract for immediate delivery.

This is shown at 156. If, at 158, a contract is sold, the fulfillment contract, which contains the fulfillment instructions, is delivered to the fulfiller at 162. Funds to pay for the order must then be deposited by the purchaser of the contract. The fulfillment house then delivers the product to the customer in accordance with the specifications in the fulfillment contract as shown at 164. However, this is done in a manner described with regard to Example 1 to maintain anonymity. First, the encryption process takes place at 163 then the carrier delivery activities takes place at 165. Upon fulfillment confirmation at 166, funds are transferred from the clearinghouse to the fulfiller. Finally, the system databases are updated at 168 with information about the transaction.

If the fulfillment contract is not sold at 158, the fulfiller may update the price and quantity at 160 and return it for submission to the process at 152 with the new price and quantity. The fulfiller may also decide not to change the price and quantity and return it to System Trading Module 42 to await sale of the fulfillment contract at 156.

By using this procedure, shipping costs are reduced if the fulfiller is geographically closer to the customer than the retailer. Furthermore, the retailer has reduced his need to hold physical inventory by outsourcing order fulfillment responsibility to the fulfiller.

Example 3: Shipping Arbitrage

This Example is directed to the situation in which a retailer has a customer located a long geographical distance away. The retailer in this situation desires to save on shipping costs without effecting inventory or revenue flow. More specifically, the retailer wishes to "book the sale" as though it was delivered from its own inventory. In this case, the retailer simultaneously carries out Examples 1 and 2. In doing this, the retailer purchases a fulfillment contract from a fulfiller located close to his customer, and simultaneously sells a fulfillment contract to some other third party on the system whose customer is located closer to the retailer. The fulfiller then delivers the product to the retailer's customer, while the retailer delivers the identical product to the third party's customer. This transaction saves shipping costs effecting neither sales nor inventory levels. Example 4: Buying and Exercising Options

This Example is directed to the situation in which a retailer prefers to minimize physical inventory holding, yet wants to avoid the risk of not having access to reasonably priced inventory as orders come in. By using the trading system, retailer may purchase fulfillment contracts to fulfill orders as orders come in, but, there is a risk that products would not be available on the spot market at a reasonable price. By purchasing options on spot market inventory (virtual inventory), the retailer can assure availability of inventory at a predetermined strike or exercise price. These option contracts give retailers a method to reduce risks due to fluctuating supply, demand, and prices of inventory. To do this, the system pools the options contracts, and, as such, they are not associated with any particular fulfillment vendor until the options are exercised. Therefore, both the virtual inventory and spot market inventory offer the same opportunities for shipping cost savings via shipping arbitrage. In this example, the retailer will have already provided Dealer Logistics

Planning Module 32 with full information about supply and demand trends, product costs, and promotions for this product. Dealer Logistics Planning Module 32 then assists the retailer in assessing its internal inventory costs and availability of storage for the product. System Trading Module 42 provides the retailer with information about costs and availability of virtual inventory across the network. Based on all this information, the Dealer Logistics Planning Module makes recommendations regarding the quantity of virtual inventory options to be purchased. Of course, this recommendation may be overridden by the retailer (or wholesaler) who has the final decision about the action to be taken. If the retailer decides to order virtual inventory options, then the order is entered into System Trading Module 42, which places the order either directly with a fulfillment house or other holder of inventory, or indirectly through a market- maker (if present). Funds to pay for the options must be deposited with a clearinghouse. Later, as customers purchase items from the retailer, the virtual inventory options are exercised, converting them to fulfillment contracts for immediate delivery at the nearest fulfiller with available inventory. At that time, the "strike price" of the option must be paid to the clearinghouse to release the item. The fulfillment house then delivers the product to the customer, and funds are transferred from the clearinghouse to the fulfiller. The deliveries are done in the manner described to maintain anonymity.

By using this procedure, shipping costs are reduced if the fulfiller (or other inventory holder) is geographically closer than the retailer (or wholesaler) to the ultimate customer (or "brick-and-mortar" retail destination). Furthermore, the retailer (or wholesaler) has reduced his need to hold inventory, transferring this responsibility to the fulfiller (or other inventory holder) that may be more efficient. Example 5: Writing and Selling Option Referring to Figure 5, Example 5 will be described. This Example is direct to a fulfiller who prefers to hold inventory and deliver it to customers belonging to other retailers. By using the spot market trading system, the fulfiller may sell fulfillment contracts on a routine basis. However, there are risks that demand might not materialize, or that price might drop leaving the fulfiller with high inventory levels. By entering into virtual inventory options contracts, the fulfiller may cover part of his inventory carrying costs, ensure a basic demand level, and reduce risk.

Again referring to Figure 5, the system is initiated at 250. At this time, the fulfiller may provide Dealer Logistics Planning Module 32 with full information about supply and demand trends, product costs, and promotions for products. The Dealer Logistics Planning Module assists the fulfiller in assessing its internal inventory costs and availability of storage for the product. The fulfiller develops a forecast of expected product sales and demand at 252. System Trading Module 42 provides the fulfiller with information about current pricing for virtual inventory across the network. Based on this information, the Logistics Planning System makes recommendations regarding the quantity of virtual inventory options to be sold. These recommendations may or may not be taken by the fulfiller who has the final decision about the action to be taken.

If the fulfiller decides to sell virtual inventory options, then the order is entered into System Trading Module 42 at 254. This places the order either directly with a retailer or other dealer or indirectly through a market-maker (if present). In the same manner as selling a fulfillment contract that was described above, selling an option on a fulfillment contract may involve placing an offer and waiting for a bid from a retailer to complete the transaction, if there is no market- maker in the system. At 256, the fulfiller waits for the option to be exercised. As shown at 258, if the call option is not exercised over time, it must be determined if the call option expired. If it has not expired at 262, and the fulfiller does not desire to cancel its short position, it returns to 256 and again awaits the exercise of an option. However, at any time, the fulfiller may effectively cancel the option by purchasing an offsetting call option in the same product with the same expiration as shown at 264. If this happens, the next step is to use the GLOW system to update the system databases at 278, and the transaction is ended at 280.

At 266, if a retailer holding an option receives an order from a customer, the retailer might exercise the option, converting it to a fulfillment contract for immediate delivery. At that time, the fulfillment house receives delivery instructions as shown at 268. Then, at 268, the fulfillment house begins the determination of the fulfillment source. The fulfiller reviews its own inventory and market conditions to determine whether to fulfill the order from its own inventory or whether to purchase inventory from another fulfiller. Therefore, at 270, if it is determined that spot market will be fulfiller source, then the writer will provide a fulfillment contract to fill the order at 274. As such, for purpose of delivery, there is encryption at 275 and carrier activities at 247 in the manner described above for maintaining anonymity. The fulfiller then sends a confirmation of delivery of the product to the writer. Taking this, the GLOW system updates the system database at 278 and the transaction is terminated at 280.

If, on the other hand, the source is the writer's own inventory, the order is filled from that inventory and as the fulfiller the writer at 276 sends confirmation that the order has been filled. Although not shown, if necessary the process for maintaining anonymity is used. Next, the GLOW system updates the system database at 276 and the transaction is terminated at 280. When the confirmation is sent to the retailer at 276, funds are transferred from the clearinghouse to the fulfiller.

By using the present invention, shipping costs are reduced if the fulfiller (or other inventory holder) is geographically closer than the retailer (or wholesaler) to the ultimate customer (or "brick-and-mortar" retail destination). Furthermore, the fulfiller has improved his her economy of scale, smoothed his/her inventory flows, and reduced his/her average costs, by taking advantage of the opportunity to carry out logistics tasks on behalf of other retail merchants. Example 6: Inventory Optimization and Shipping Arbitrage

The situation in Example 6 involves a dealer who both holds inventory and sells and delivers products to customer. This dealer wishes to save on shipping costs and also minimize his her physical inventory costs and total inventory costs. This may be achieved by the dealer simultaneously using the methods of Example 4 and 5. That is, the dealer purchases and sells options on fulfillment contracts.

The dealer will select the specific mix and timing of purchases and sales based on the market factors. As orders arrive, they are filled optimally from either real or virtual inventory, and orders placed with other merchants are also delivered from real inventory as sold options are converted. This type of transaction method saves shipping costs and allows the dealer to take advantage of a far-flung network of inventory sources while operating with reduced safety stock.

The terms and expressions that are employed herein are terms or description and not of limitation. There is no intention in the use of such terms and expressions of excluding the equivalents of the feature shown or described, or portions thereof, it being recognized that various modifications are possible within the scope of the invention as claimed.

Claims

CLAIMS:

1. A logistics optimization system, comprising:

(a) dealer product costing module for generating information relating to costs associated with at least a first product for a first dealer and for multiple dealers;

(b) dealer modeling module for generating information relating to a predictive model of future sales for at least the first product;

(c) dealer forecasting module for generating information relating to an estimate of future demand and future demand variability, and planned future marketing promotions for at least the first product;

^• (d) system pricing module for generating information relating to estimates of costs of option contracts according to spot market pricing data;

(e) system quotes storage structure for storing information relating to an availability and pricing of spot market inventory and options on spot inventory based on the information in the system pricing module and system information relating to matched bids and offers for inventory options;

(f) dealer logistics planning module for generating information relating to logistics action plans based on information from the dealer product costing module, dealer forecasting module, and system quote storage structure;

(g) dealer trading module for generating bids to buy inventory options and offers to sell inventory options; and

(h) system trading module for generating matches of dealer bids to buy inventory options with dealer offers to sell inventory options based on the bids or offers from the dealer trading module and information from the system quotes storage structure.

2. The logistics system as recited in claim 1, wherein inputs to the dealer product costing module include information relating to at least the first product and information relating to at least the first dealer.

3. The logistics system as recited in claim 1 , wherein the costs associated with at least a first product is determined according to the Expression

C ⁼ T carry) ( P_Product) where, fcarry ~ ' capital + t ervice + ^"storage + isk Pproduct ⁼ Pacquisition + Ptransport + P handling

4. The logistics system as recited in claim 1, wherein inputs to the dealer modeling module include dealer marketing history information and dealer sales history information relating to at least the first dealer.

5. The logistics system as recited in claim 4, wherein the dealer modeling module uses regression analysis, exponential smoothing, or neural network modeling to generate the predictive model of future sales of at least the first product.

6. The logistics system as recited in claim 1 wherein, the system pricing module uses the Black-Scholes equation modified to account for carrying costs and the right to exercise options before expiration.

7. The logistics system as recited in claim 1 wherein, the Black- Scholes equation modified to account for carrying costs and the right to exercise options before expiration is according to the Expression dv . l ,„, av . ( \„ dV dt ^{+ σ2s 2} ^ ⁺ ^^ ^{+ r}^^S^^{~ r}^^^{V ≤ 0} where,

N = The value of an option at time "t."

S = Spot market price of an underlying asset. t = Time. σ = Volatility of underlying- asset.

^i_nterest = Risk free interest rate. r _car_ry = Carrying cost.

8. The logistics system as recited in claim 1 wherein, the dealer logistics planning module generates logistics action plans according to optimal inventory and trading policies.

9. The logistics system as recited in claim 8 wherein, a logistics action plan will include recommendations for physical and virtual inventory.

10. The logistics system as recited in claim 8 wherein, virtual inventory includes options on spot market inventory or spot market inventory for immediate order fulfillment.

11. A logistics optimization method, comprising the steps of:

(a) pooling physical inventory among a plurality of system users, with each system user maintaining a physical inventory level that is less than what is projected for that system user to meet a predetermined service level; and (b) each system user purchasing and/or selling options on order fulfillment contracts to form virtual inventory to be added to the physical inventory level to meet the predetermined service level.

12. A logistics optimization method, comprising the steps of:

(a) pooling physical inventory among a plurality of system users;

(b) each system user maintaining inventory by physical inventory and/or virtual inventory; and

(c) each system user fulfilling orders from one or more of physical inventory, virtual inventory purchased from another system user based on an exercise of call options, or order fulfillment contracts purchased on a spot market.

13. A logistics optimization method, comprising the steps of:

(a) pooling physical inventory among a plurality of system users;

(c) at least one system user fulfilling orders of other system users from one or more of physical inventory or virtual inventory by accepting bids for order fulfillment contracts.

14. A logistics optimization method, comprising the steps of:

(a) pooling physical inventory among a plurality of system users;

(c) at least one system user fulfilling orders by exercising virtual inventory call options purchased from another system user.

15. A logistics optimization method, comprising the steps of:

(a) pooling physical inventory among a plurality of system users;

(c) at least one system user fulfilling orders by exercising virtual inventory call options purchased from another system user when the other system user is geographically closer to a final delivery location of an order.

16. A logistics optimization method, comprising the steps of:

(a) pooling physical inventory among a plurality of system users;

(b) each system user maintaining inventory by physical inventory and/or virtual inventory; and (c) at least one system user fulfilling orders of other system users from one or more of physical inventory or virtual inventory by accepting bids for order fulfillment contracts when the system user fulfilling the order is geographically closer to a final delivery location of an order.

17. A logistics optimization system, comprising:

(a) dealer product costing module for generating information relating to costs associated with at least a first product for a first dealer and from multiple dealers;

(b) dealer modeling module for generating information relating to a predictive model of future sales of at least the first product;

(d) system pricing module for generating information relating to estimates of costs of option contracts according to spot market pricing data;

(g) dealer trading module for generating between bids to buy inventory options and offers to sell inventory options;

(h) system trading module for generating matches of dealer bids to buy inventory options with dealer offers to sell inventory options based on the bids or offers from the dealer trading module and information from the system quotes storage structure; and

(i) encryption means for conducting order fulfillment anonymously between the dealers.

18. The logistics system as recited in claim 17, wherein inputs to the dealer product costing module include information relating to at least the first product and information relating to at least the first dealer.

19. The logistics system as recited in claim 17, wherein the costs associated with at least a first product is determined according to the Expression ⁼ ( ϊ ^'carry) { P product) where,

Icarry ⁼ r_capϊtal + r_service "+" ^storage + Trisk Pproduct ~ P acquisition + transport + Pliandltng

20. The logistics system as recited in claim 17, wherein inputs to the dealer modeling module include dealer marketing history information and dealer sales history information relating to at least the first dealer.

21. The logistics system as recited in claim 20, wherein the dealer modeling module uses regression analysis, exponential smoothing, or neural network modeling to generate the predictive model of future sales of at least the first product.

22. The logistics system as recited in claim 17 wherein, the system pricing module uses the Black-Scholes equation modified to account for carrying costs and the right to exercise options before expiration.

23. The logistics system as recited in claim 17 wherein, the Black- Scholes equation modified to account for carrying costs and the right to exercise options before expiration is according to the Expression dV 1 _{2 f},₂ 3V / \_a dV _{T7 ^ Λ}

^"aT ⁺ 2 ds^⁺ ^^{interest + rcaιry P~}ds ^{~ rinterest} where,

V = The value of an option at time "t."

S = Spot market price of an underlying asset. t = Time. σ = Volatility of underlying asset. rintere t = Risk free interest rate. r carry = Carrying cost.

24. The logistics system as recited in claim 17 wherein, the dealer logistics planning module generates logistics action plans according to optimal inventory and trading policies.

25. The logistics system as recited in claim 24 wherein, a logistics action plan will include recommendations for physical and virtual inventory.

26. The logistics system as recited in claim 24 wherein, virtual inventory includes options on spot market inventory or spot market inventory for immediate order fulfillment.

27. A logistics optimization method, comprising the steps of:

(a) pooling physical inventory among a plurality of system users, with each system user maintaining a physical inventory level that is less than what is projected for that system user to meet a predetermined service level;

(b) each system user purchasing and/or selling options on order fulfillment confracts to form virtual inventory to be added to the physical inventory level to meet the predetermined service level;

(c) each system user anonymously purchasing and selling options on order fulfillment contracts at step (b).

28. A logistics optimization method, comprising the steps of:

(a) pooling physical inventory among a plurality of system users;

(b) each system user maintaining inventory by physical inventory and/or virtual inventory;

(c) each system user fulfilling orders from one or more of physical inventory, virtual inventory purchased from another system user based on an exercise of call options, or order fulfillment contracts purchased on a spot market; and

(d) each system user anonymously fulfilling orders at step (c).

29. A logistics optimization method, comprising the steps of:

(a) pooling physical inventory among a plurality of system users;

(c) at least one system user fulfilling orders of other system users from one or more of physical inventory or virtual inventory by accepting bids for order fulfillment contracts; and

(d) the system user anonymously fulfilling orders at step (c).

30. A logistics optimization method, comprising the steps of:

(a) pooling physical inventory among a plurality of system users;

(b) each system user maintaining inventory by physical inventory and/or virtual inventory; (c) at least one system user fulfilling orders by exercising virtual inventory call options purchased from another system user; and

(d) the system user anonymously fulfilling orders at step (c).

31. A logistics optimization method, comprising the steps of:

(a) pooling physical inventory among a plurality of system users;

(c) at least one system user fulfilling orders by exercising virtual inventory call options purchased from another system user when the other system user is geographically closer to a final delivery location of an order; and

(d) the system user anonymously fulfilling orders at step (c).

32. A logistics optimization method, comprising the steps of:

(a) pooling physical inventory among a plurality of system users;

(c) at least one system user fulfilling orders of other system users from one or more of physical inventory or virtual inventory by accepting bids for order fulfillment contracts when the system user fulfilling the order is geographically closer to a final delivery location of an order; and

(d) the system user anonymously fulfilling orders at step (c).

33. A logistics optimization system, comprising:

(a) costing module for generating information relating to costs associated with at least a first item for a first system user and for multiple system users;

(b) modeling module for generating information relating to a predictive model of future sales for at least the first item;

(c) forecasting module for generating information relating to an estimate of future demand and future demand variability, and planned future marketing promotions for at least the first item;

(d) system quotes module for providing information relating to matched bids and offers for items;

(e) logistics planning module for generating information relating to logistics action plans based on information from the costing module, forecasting module, and system quotes module; (f) first trading module for generating bids to buy and offers to sell items; and

(g) system trading module for generating matches of bids to buy items with offers to sell items based on the bids or offers from the first trading module and information from the system quotes module.

34. The system as recited in claim 33, wherein the items include options contracts.

35. The system as recited in claim 34, wherein system risk management is provided by using options contracts to guarantee access to items at a predetermined price.

36. The system as recited in claim 34, wherein a system user that purchases options contracts can speculate on future selling prices of such options on a spot market.

37. The system as recited in claim 34, wherein a system user can sell options contracts on a spot market to speculate on future selling prices on the spot market.